A novel approach to the derivation of fuzzy membership functions using the Falcon-MART architecture

Abstract

A fuzzy neural network, Falcon-MART, is proposed in this paper. This is a modification of the original Falcon-ART architecture. Both Falcon-ART and Falcon-MART are fuzzy neural networks that can be used as fuzzy controllers or applied to areas such as forgery detection, pattern recognition and data analysis. They constitute a group of hybrid systems that incorporate fuzzy logic into neural networks. In this way, the structure of these hybrid networks become transparent as high level IF-THEN human-like reasoning is used to interpret the network connections. In addition, the hybrid networks automatically derive the fuzzy rules (knowledge base) of the problem domain using neural network techniques and hence avoid the pitfalls of traditional fuzzy systems. The main problem in designing a fuzzy neural network is how to formulate the fuzzy rule base. Most proposed fuzzy neural networks in the literature could be classified into two categories. The first group assumes the existence of a preliminary rule base and uses neural techniques to tune the parameters to obtain the final set of fuzzy rules. The second group assumes no knowledge of any fuzzy rules and performs a cluster analysis on the numerical training data before formulating the rules from the computed clusters. Falcon-ART attempts to overcome the constraints faced by these two groups of fuzzy neural networks by using the fuzzy ART technique to partition the training data set. However, there are several shortcomings in the Falcon-ART network. They are: 1. Poor network performances when the classes of input data are closely similar to each other; 2. Weak resistance to noisy/spurious training data; 3. Termination of network training process depends heavily on a preset error parameter; and 4. Learning efficiency may deteriorate as a result of using complementary coded training data. Falcon-MART has been developed to address these shortcomings. To evaluate the effectiveness of Falcon-MART, three different sets of experiments are conducted. The first experiment demonstrates the efficiency of Falcon-MART over Falcon-ART using the Fisher's Iris data set. The second experiment evaluates the modeling capability of Falcon-MART against the classical multi-layered perceptron (MLP) network using a set of traffic flow data. The last experiment uses a set of phoneme data to demonstrate the clustering ability of Falcon-MART against the traditional K-nearest-neighbor ( K-NN) classifier. The results obtained are encouraging.